Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "29" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 32 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 32 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460015 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.673136 | -0.218896 | 2.960582 | 0.740496 | 0.398640 | 0.672681 | 2.710581 | 1.793769 | 0.5863 | 0.5967 | 0.3506 | nan | nan |
| 2460014 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.860591 | 0.056099 | 2.221483 | 0.578669 | -0.667036 | -0.692594 | 6.634328 | 2.066531 | 0.5558 | 0.5760 | 0.3557 | nan | nan |
| 2460013 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.199711 | -0.436013 | 2.920613 | 0.685376 | 0.054300 | 0.657973 | 5.875022 | 2.534233 | 0.5813 | 0.5948 | 0.3583 | nan | nan |
| 2460012 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.723448 | -0.282923 | 2.790202 | 0.497689 | -0.037949 | 1.381129 | 6.189164 | 2.667360 | 0.5683 | 0.5885 | 0.3518 | nan | nan |
| 2460011 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.875593 | -0.324542 | 3.451029 | 0.491017 | -0.199988 | 2.170241 | 5.532759 | 1.965434 | 0.5632 | 0.5797 | 0.3638 | nan | nan |
| 2460010 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.973054 | 16.492027 | 11.280038 | 12.039815 | 9.211891 | 10.490235 | 1.231543 | 0.702894 | 0.0311 | 0.0380 | 0.0074 | nan | nan |
| 2460009 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.055280 | 15.256362 | 12.600465 | 13.276645 | 7.327876 | 8.857982 | 1.027398 | 1.032333 | 0.0291 | 0.0366 | 0.0076 | nan | nan |
| 2460008 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.586801 | 18.684476 | 13.774161 | 14.599124 | 6.647766 | 7.785809 | 4.474612 | 5.149168 | 0.0311 | 0.0396 | 0.0088 | nan | nan |
| 2460007 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.823295 | 13.965539 | 10.763695 | 11.413016 | 5.925715 | 7.225755 | 1.499319 | 1.099917 | 0.0296 | 0.0373 | 0.0079 | nan | nan |
| 2459999 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459998 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.199946 | 11.827002 | 9.201422 | 9.640461 | 7.989838 | 10.224410 | 0.716364 | 0.494642 | 0.0291 | 0.0346 | 0.0058 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.063480 | 12.880477 | 9.757712 | 10.372631 | 7.706795 | 9.608069 | 1.651749 | 0.796776 | 0.0294 | 0.0371 | 0.0078 | nan | nan |
| 2459996 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.175181 | 13.887099 | 12.278213 | 12.723323 | 7.299406 | 9.289128 | 0.355362 | 0.208221 | 0.0293 | 0.0364 | 0.0072 | nan | nan |
| 2459995 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.389126 | 14.075994 | 11.367032 | 11.924324 | 8.056884 | 9.499550 | 0.309850 | 0.059366 | 0.0305 | 0.0398 | 0.0093 | nan | nan |
| 2459994 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.934681 | 13.652973 | 9.800971 | 10.441341 | 7.784242 | 9.555188 | 0.191287 | -0.168637 | 0.0294 | 0.0350 | 0.0058 | nan | nan |
| 2459993 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.154728 | 12.937110 | 9.089315 | 9.653605 | 10.172296 | 10.910391 | 0.699025 | 1.433475 | 0.0282 | 0.0300 | 0.0023 | nan | nan |
| 2459991 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.000774 | 15.930090 | 9.640470 | 10.228727 | 9.186640 | 10.765426 | 0.167960 | -0.191867 | 0.0298 | 0.0345 | 0.0051 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.553158 | 13.113660 | 9.433381 | 9.923356 | 9.091612 | 11.051529 | 0.036034 | -0.515284 | 0.0301 | 0.0370 | 0.0071 | nan | nan |
| 2459989 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.349240 | 13.299594 | 8.395098 | 9.083031 | 8.011723 | 9.254020 | -0.154635 | -0.458927 | 0.0290 | 0.0342 | 0.0052 | nan | nan |
| 2459988 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.398332 | 15.572353 | 9.732878 | 10.198041 | 10.783178 | 13.209987 | -0.004532 | -0.291598 | 0.0293 | 0.0337 | 0.0048 | nan | nan |
| 2459987 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.165210 | 13.023057 | 9.452410 | 10.088421 | 6.397936 | 7.985347 | 0.580332 | 0.509502 | 0.0297 | 0.0368 | 0.0072 | nan | nan |
| 2459986 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.737957 | 15.981156 | 10.351597 | 10.878490 | 9.409049 | 11.280250 | 5.398854 | 9.061539 | 0.0296 | 0.0357 | 0.0063 | nan | nan |
| 2459985 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.673496 | 14.447803 | 9.597182 | 10.142721 | 7.247751 | 8.627456 | 0.914978 | 0.279220 | 0.0296 | 0.0352 | 0.0059 | nan | nan |
| 2459984 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.065185 | 13.883228 | 9.963977 | 10.519435 | 9.401187 | 12.084102 | 2.007096 | 1.935960 | 0.0302 | 0.0379 | 0.0079 | nan | nan |
| 2459983 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.858272 | 13.588152 | 9.518185 | 9.929912 | 9.305910 | 11.178839 | 2.736760 | 5.457480 | 0.0304 | 0.0364 | 0.0062 | nan | nan |
| 2459982 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.011772 | 10.741297 | 8.091572 | 8.500736 | 4.521930 | 5.268095 | 2.385619 | 3.106478 | 0.0297 | 0.0354 | 0.0061 | nan | nan |
| 2459981 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.145372 | 12.523065 | 10.131561 | 10.547460 | 10.463778 | 12.375013 | 0.147174 | -0.206662 | 0.0306 | 0.0373 | 0.0070 | nan | nan |
| 2459980 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.978068 | 12.169329 | 9.110042 | 9.655290 | 9.050753 | 10.803711 | 5.080731 | 5.071647 | 0.0294 | 0.0351 | 0.0059 | nan | nan |
| 2459979 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.342276 | 12.602418 | 8.422282 | 9.016451 | 8.964361 | 10.126014 | 0.351468 | 0.046165 | 0.0306 | 0.0337 | 0.0049 | nan | nan |
| 2459978 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.451572 | 12.818719 | 9.150549 | 9.706434 | 9.361476 | 10.968398 | -0.097460 | -0.396639 | 0.0294 | 0.0335 | 0.0045 | nan | nan |
| 2459977 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459976 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.672701 | 13.043024 | 9.466703 | 9.970165 | 9.443827 | 10.854611 | 0.631887 | 0.240088 | 0.0297 | 0.0349 | 0.0055 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Power | 2.960582 | -0.218896 | -1.673136 | 0.740496 | 2.960582 | 0.672681 | 0.398640 | 1.793769 | 2.710581 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Temporal Discontinuties | 6.634328 | -1.860591 | 0.056099 | 2.221483 | 0.578669 | -0.667036 | -0.692594 | 6.634328 | 2.066531 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Temporal Discontinuties | 5.875022 | -1.199711 | -0.436013 | 2.920613 | 0.685376 | 0.054300 | 0.657973 | 5.875022 | 2.534233 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Temporal Discontinuties | 6.189164 | -0.723448 | -0.282923 | 2.790202 | 0.497689 | -0.037949 | 1.381129 | 6.189164 | 2.667360 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Temporal Discontinuties | 5.532759 | -0.875593 | -0.324542 | 3.451029 | 0.491017 | -0.199988 | 2.170241 | 5.532759 | 1.965434 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 16.492027 | 12.973054 | 16.492027 | 11.280038 | 12.039815 | 9.211891 | 10.490235 | 1.231543 | 0.702894 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 15.256362 | 12.055280 | 15.256362 | 12.600465 | 13.276645 | 7.327876 | 8.857982 | 1.027398 | 1.032333 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 18.684476 | 18.684476 | 14.586801 | 14.599124 | 13.774161 | 7.785809 | 6.647766 | 5.149168 | 4.474612 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.965539 | 10.823295 | 13.965539 | 10.763695 | 11.413016 | 5.925715 | 7.225755 | 1.499319 | 1.099917 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 11.827002 | 9.199946 | 11.827002 | 9.201422 | 9.640461 | 7.989838 | 10.224410 | 0.716364 | 0.494642 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 12.880477 | 10.063480 | 12.880477 | 9.757712 | 10.372631 | 7.706795 | 9.608069 | 1.651749 | 0.796776 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.887099 | 11.175181 | 13.887099 | 12.278213 | 12.723323 | 7.299406 | 9.289128 | 0.355362 | 0.208221 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 14.075994 | 11.389126 | 14.075994 | 11.367032 | 11.924324 | 8.056884 | 9.499550 | 0.309850 | 0.059366 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.652973 | 10.934681 | 13.652973 | 9.800971 | 10.441341 | 7.784242 | 9.555188 | 0.191287 | -0.168637 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 12.937110 | 12.154728 | 12.937110 | 9.089315 | 9.653605 | 10.172296 | 10.910391 | 0.699025 | 1.433475 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 15.930090 | 13.000774 | 15.930090 | 9.640470 | 10.228727 | 9.186640 | 10.765426 | 0.167960 | -0.191867 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.113660 | 13.113660 | 10.553158 | 9.923356 | 9.433381 | 11.051529 | 9.091612 | -0.515284 | 0.036034 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.299594 | 13.299594 | 10.349240 | 9.083031 | 8.395098 | 9.254020 | 8.011723 | -0.458927 | -0.154635 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 15.572353 | 15.572353 | 12.398332 | 10.198041 | 9.732878 | 13.209987 | 10.783178 | -0.291598 | -0.004532 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.023057 | 10.165210 | 13.023057 | 9.452410 | 10.088421 | 6.397936 | 7.985347 | 0.580332 | 0.509502 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 15.981156 | 15.981156 | 12.737957 | 10.878490 | 10.351597 | 11.280250 | 9.409049 | 9.061539 | 5.398854 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 14.447803 | 14.447803 | 11.673496 | 10.142721 | 9.597182 | 8.627456 | 7.247751 | 0.279220 | 0.914978 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.883228 | 11.065185 | 13.883228 | 9.963977 | 10.519435 | 9.401187 | 12.084102 | 2.007096 | 1.935960 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.588152 | 10.858272 | 13.588152 | 9.518185 | 9.929912 | 9.305910 | 11.178839 | 2.736760 | 5.457480 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 10.741297 | 9.011772 | 10.741297 | 8.091572 | 8.500736 | 4.521930 | 5.268095 | 2.385619 | 3.106478 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 12.523065 | 12.523065 | 10.145372 | 10.547460 | 10.131561 | 12.375013 | 10.463778 | -0.206662 | 0.147174 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 12.169329 | 12.169329 | 9.978068 | 9.655290 | 9.110042 | 10.803711 | 9.050753 | 5.071647 | 5.080731 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 12.602418 | 10.342276 | 12.602418 | 8.422282 | 9.016451 | 8.964361 | 10.126014 | 0.351468 | 0.046165 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 12.818719 | 12.818719 | 10.451572 | 9.706434 | 9.150549 | 10.968398 | 9.361476 | -0.396639 | -0.097460 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | N01 | digital_ok | nn Shape | 13.043024 | 13.043024 | 10.672701 | 9.970165 | 9.466703 | 10.854611 | 9.443827 | 0.240088 | 0.631887 |